The present disclosure relates to modeling of incident occurrences related to a service provider, such as a transportation system. More specifically, the present disclosure relates to regression modeling of incident occurrences that impact quality of service provided by the service provider.
Many service providers monitor and analyze analytics related to the services they provide. For example, computer aided dispatch/automated vehicle location (CAD/AVL) is a system in which public transportation vehicle positions are determined through a global positioning system (GPS) and transmitted to a central server located at a transit agency's operations center and stored in a database for later use. The CAD/AVL system also typically includes two-way radio communication by which a transit system operator can communicate with vehicle drivers. The CAD/AVL system may further log and transmit incident information including an event identifier (ID) and a time stamp related to various events that occur during operation of the vehicle. For example, for a public bus system, logged incidents can include door opening and closing, driver logging on or off, wheel chair lift usage, bike rack usage, current bus condition, and other similar events. Some incidents are automatically logged by the system as they are received from vehicle on-board diagnostic systems or other data collection devices, while others are entered into the system manually by the operator of the vehicle.
For a typical public transportation company, service reliability is defined as variability of service attributes. Problems with reliability are ascribed to inherent variability in the system, especially demand for transit, operator performance, traffic, weather, road construction, crashes, and other similar unavoidable or unforeseen events. As transportation providers cannot control all aspects of operation owing to these random and unpredictable disturbances, they must adjust to the disturbances to maximize reliability. Several components that determine reliable service are schedule adherence, maintenance of uniform headways (e.g., the time between vehicles arriving in a transportation system), minimal variance of maximum passenger loads, and overall trip times. However, most public transportation companies put a greater importance on schedule adherence.
By using a CAD/AVL system, transit operators can easily obtain current and historical operation information related to a vehicle or a fleet of vehicles. However, the information shows an overall trend of the data, not individual data related to specific incidents that may occur during the operation of a vehicle. For example, the historical information may show how well a vehicle adhered to a set schedule over a period of time (e.g., three months), but the information does not provide an easy way to determine cause of unreliability and the relationship between reliability and passenger travel behavior, nor does the information provide an understanding of the effect of unreliability on operational costs.